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The ethics of big data as a public good: which public? Whose good?
Author(s) -
Linnet Taylor
Publication year - 2016
Publication title -
philosophical transactions of the royal society a mathematical physical and engineering sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.074
H-Index - 169
eISSN - 1471-2962
pISSN - 1364-503X
DOI - 10.1098/rsta.2016.0126
Subject(s) - big data , data sharing , value (mathematics) , public value , public relations , incentive , public good , intermediary , open data , argument (complex analysis) , business , political science , computer science , economics , marketing , law , medicine , alternative medicine , pathology , machine learning , microeconomics , operating system , biochemistry , chemistry
International development and humanitarian organizations are increasingly calling for digital data to be treated as a public good because of its value in supplementing scarce national statistics and informing interventions, including in emergencies. In response to this claim, a ‘responsible data’ movement has evolved to discuss guidelines and frameworks that will establish ethical principles for data sharing. However, this movement is not gaining traction with those who hold the highest-value data, particularly mobile network operators who are proving reluctant to make data collected in low- and middle-income countries accessible through intermediaries. This paper evaluates how the argument for ‘data as a public good’ fits with the corporate reality of big data, exploring existing models for data sharing. I draw on the idea of corporate data as an ecosystem involving often conflicting rights, duties and claims, in comparison to the utilitarian claim that data's humanitarian value makes it imperative to share them. I assess the power dynamics implied by the idea of data as a public good, and how differing incentives lead actors to adopt particular ethical positions with regard to the use of data. This article is part of the themed issue ‘The ethical impact of data science’.

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